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Zero
File size: 8,680 Bytes
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# Based on code from: https://github.com/zhenye234/xcodec
# Licensed under MIT License
# Modifications by BosonAI
import torch
import torch.nn as nn
class Conv1d1x1(nn.Conv1d):
"""1x1 Conv1d."""
def __init__(self, in_channels, out_channels, bias=True):
super(Conv1d1x1, self).__init__(in_channels, out_channels, kernel_size=1, bias=bias)
class Conv1d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
padding: int = -1,
dilation: int = 1,
groups: int = 1,
bias: bool = True,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
if padding < 0:
padding = (kernel_size - 1) // 2 * dilation
self.dilation = dilation
self.conv = nn.Conv1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
bias=bias,
)
def forward(self, x):
"""
Args:
x (Tensor): Float tensor variable with the shape (B, C, T).
Returns:
Tensor: Float tensor variable with the shape (B, C, T).
"""
x = self.conv(x)
return x
class ResidualUnit(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size=3,
dilation=1,
bias=False,
nonlinear_activation="ELU",
nonlinear_activation_params={},
):
super().__init__()
self.activation = getattr(nn, nonlinear_activation)(**nonlinear_activation_params)
self.conv1 = Conv1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=1,
dilation=dilation,
bias=bias,
)
self.conv2 = Conv1d1x1(out_channels, out_channels, bias)
def forward(self, x):
y = self.conv1(self.activation(x))
y = self.conv2(self.activation(y))
return x + y
class ConvTranspose1d(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int,
padding=-1,
output_padding=-1,
groups=1,
bias=True,
):
super().__init__()
if padding < 0:
padding = (stride + 1) // 2
if output_padding < 0:
output_padding = 1 if stride % 2 else 0
self.deconv = nn.ConvTranspose1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
output_padding=output_padding,
groups=groups,
bias=bias,
)
def forward(self, x):
"""
Args:
x (Tensor): Float tensor variable with the shape (B, C, T).
Returns:
Tensor: Float tensor variable with the shape (B, C', T').
"""
x = self.deconv(x)
return x
class EncoderBlock(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
stride: int,
dilations=(1, 1),
unit_kernel_size=3,
bias=True,
):
super().__init__()
self.res_units = torch.nn.ModuleList()
for dilation in dilations:
self.res_units += [
ResidualUnit(
in_channels,
in_channels,
kernel_size=unit_kernel_size,
dilation=dilation,
)
]
self.num_res = len(self.res_units)
self.conv = Conv1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3 if stride == 1 else (2 * stride), # special case: stride=1, do not use kernel=2
stride=stride,
bias=bias,
)
def forward(self, x):
for idx in range(self.num_res):
x = self.res_units[idx](x)
x = self.conv(x)
return x
class Encoder(nn.Module):
def __init__(
self,
input_channels: int,
encode_channels: int,
channel_ratios=(1, 1),
strides=(1, 1),
kernel_size=3,
bias=True,
block_dilations=(1, 1),
unit_kernel_size=3,
):
super().__init__()
assert len(channel_ratios) == len(strides)
self.conv = Conv1d(
in_channels=input_channels,
out_channels=encode_channels,
kernel_size=kernel_size,
stride=1,
bias=False,
)
self.conv_blocks = torch.nn.ModuleList()
in_channels = encode_channels
for idx, stride in enumerate(strides):
out_channels = int(encode_channels * channel_ratios[idx]) # could be float
self.conv_blocks += [
EncoderBlock(
in_channels,
out_channels,
stride,
dilations=block_dilations,
unit_kernel_size=unit_kernel_size,
bias=bias,
)
]
in_channels = out_channels
self.num_blocks = len(self.conv_blocks)
self.out_channels = out_channels
def forward(self, x):
x = self.conv(x)
for i in range(self.num_blocks):
x = self.conv_blocks[i](x)
return x
class DecoderBlock(nn.Module):
"""Decoder block (no up-sampling)"""
def __init__(
self,
in_channels: int,
out_channels: int,
stride: int,
dilations=(1, 1),
unit_kernel_size=3,
bias=True,
):
super().__init__()
if stride == 1:
self.conv = Conv1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3, # fix kernel=3 when stride=1 for unchanged shape
stride=stride,
bias=bias,
)
else:
self.conv = ConvTranspose1d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(2 * stride),
stride=stride,
bias=bias,
)
self.res_units = torch.nn.ModuleList()
for idx, dilation in enumerate(dilations):
self.res_units += [
ResidualUnit(
out_channels,
out_channels,
kernel_size=unit_kernel_size,
dilation=dilation,
)
]
self.num_res = len(self.res_units)
def forward(self, x):
x = self.conv(x)
for idx in range(self.num_res):
x = self.res_units[idx](x)
return x
class Decoder(nn.Module):
def __init__(
self,
code_dim: int,
output_channels: int,
decode_channels: int,
channel_ratios=(1, 1),
strides=(1, 1),
kernel_size=3,
bias=True,
block_dilations=(1, 1),
unit_kernel_size=3,
):
super().__init__()
assert len(channel_ratios) == len(strides)
self.conv1 = Conv1d(
in_channels=code_dim,
out_channels=int(decode_channels * channel_ratios[0]),
kernel_size=kernel_size,
stride=1,
bias=False,
)
self.conv_blocks = torch.nn.ModuleList()
for idx, stride in enumerate(strides):
in_channels = int(decode_channels * channel_ratios[idx])
if idx < (len(channel_ratios) - 1):
out_channels = int(decode_channels * channel_ratios[idx + 1])
else:
out_channels = decode_channels
self.conv_blocks += [
DecoderBlock(
in_channels,
out_channels,
stride,
dilations=block_dilations,
unit_kernel_size=unit_kernel_size,
bias=bias,
)
]
self.num_blocks = len(self.conv_blocks)
self.conv2 = Conv1d(out_channels, output_channels, kernel_size, 1, bias=False)
def forward(self, z):
x = self.conv1(z)
for i in range(self.num_blocks):
x = self.conv_blocks[i](x)
x = self.conv2(x)
return x
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